From Data in Products to Data as Products: The Evolution of AI/ML Features into Data-Driven Product Features

–by Ariel Jalali and Chad GPT (Intern)

The recent announcement that Airbnb's Chronon platform is now open source marks a pivotal moment in our exploration of how machine learning (ML) features evolve into data-driven product features. This shift is integral to bridging digital transformation (DX) and AI transformation (AIX), moving from conventional practices to advanced, data-centric strategies.

What are ML Features?

ML features are attributes or properties of data engineered to serve as inputs in machine learning models. These features help models detect patterns, make predictions, and learn from outcomes, significantly impacting the model’s accuracy.

What are Product Features?

Product features include all functionalities that deliver value to users that help them accomplish jobs to be done , from tangible design elements to intangible software usability, crucial for user satisfaction and competitive differentiation.

Connecting ML Features and Product Features

In the intersection of digital and AI transformations, machine learning (ML) features are not just supportive elements but are becoming central components of product strategies. This transition is crucial in both Digital Transformation (DX) and AI Transformation (AIX), illustrating how traditional business models can evolve into innovative, data-driven approaches.

Digital Transformation (DX) involves the integration of digital technology into all areas of a business, fundamentally changing how operations are conducted and value is delivered. It sets the stage for utilizing technology to enhance business processes.

AI Transformation (AIX) builds upon the digital foundations laid by DX, focusing specifically on leveraging artificial intelligence to elevate business processes, customer experiences, and operational efficiencies. As ML features evolve into central product features, they enable businesses to employ AI for making real-time decisions, predicting customer behaviors, and personalizing experiences at scale. Data experiments play a crucial role in this phase, testing and refining AI applications to ensure they meet specific business goals and user needs.

Transforming Data into Business Value: A Three-Step Process

1. Drive KPI Efficiency with Data:

The initial step focuses on optimizing key performance indicators (KPIs) through data, particularly targeting capital and operational efficiencies. To achieve this, organizations implement a comprehensive Business Intelligence (BI) framework, complete with underlying data pipelines that feed into an enterprise data warehouse supplemented by data lakes. This robust data infrastructure enables the collection and analysis of vast amounts of data using machine learning and analytics. By transforming raw data into actionable insights, businesses enhance internal processes and decision-making, improving metrics like cost management, resource allocation, and productivity.

2. Launch Internal Data Products:

With a solid BI framework in place, the next phase is to develop internal data products based on the insights gained. These tools and applications are designed to solve specific internal challenges and are first deployed within the organization. This internal use allows for iterative refinement based on detailed feedback, ensuring the products are effective and robust before wider release. Proper data instrumentation ensures that every aspect of the business is measurable and observable, facilitating continuous improvement.

3. Expose to Customers as Data-Driven Products/Features:

The final step involves adapting and scaling the validated internal data products into customer-facing solutions. This transition includes making necessary adjustments to ensure the products meet market demands and regulatory requirements, are scalable, and maintain user-friendliness. Introducing these data-driven innovations to the market not only opens new revenue streams but also significantly boosts the company's competitive advantage by leveraging a well-structured BI and data management framework.

Real-World Applications and Case Studies

Case Study 1: Spotify's Discover Weekly

1. Drive KPI Efficiency with Data:

- Utilize user interaction data and ongoing data experiments to enhance key performance indicators like engagement and listening time.

- Employ ML insights to refine music recommendation algorithms through continuous experimentation.

2. Launch Internal Data Products:

- Develop Discover Weekly based on these insights to internally test and refine the feature through rigorous data experimentation.

3. Expose to Customers as Data-Driven Products/Features:

- Roll out Discover Weekly as a personalized playlist, greatly enhancing user experience and retention.

Case Study 2: Google's Smart Reply in Gmail

1. Drive KPI Efficiency with Data:

- Apply deep learning to process vast amounts of emails efficiently, improving response times and user interaction through targeted data experiments.

- Continuously refine response accuracy and contextual relevance through data-driven insights derived from experiments.

2. Launch Internal Data Products:

- Internally test Smart Reply to ensure it meets Google’s standards for functionality and user experience, utilizing data experiments to optimize performance.

3. Expose to Customers as Data-Driven Products/Features:

- Implement Smart Reply in Gmail, providing users with quick and appropriate automated responses.

Case Study 3: Energy Sector Predictive Maintenance

1. Drive KPI Efficiency with Data:

- Use ML models for predictive maintenance to enhance key operational metrics like uptime and energy production, backed by extensive data experimentation.

2. Launch Internal Data Products:

- Develop tools from these models that can be utilized across different sectors or departments.

3. Expose to Customers as Data-Driven Products/Features:

- Offer predictive maintenance services to the broader energy sector, improving efficiency and operational continuity.

Case Study 4: Amazon Demand Forecasting

1. Drive KPI Efficiency with Data:

- Deploy sophisticated algorithms to improve inventory management metrics internally, with data experiments helping to fine-tune the processes.

2. Launch Internal Data Products:

- Use internally proven demand forecasting tools to help third-party sellers manage their sales more effectively.

3. Expose to Customers as Data-Driven Products/Features:

- Make these tools available through Amazon's Seller Central, allowing sellers to benefit from advanced demand forecasting.

Case Study 5: Airbnb’s Chronon

1. Drive KPI Efficiency with Data:

- Originate Chronon to optimize Airbnb’s internal data management and operational efficiency.

- Streamline data processes for Airbnb’s ML models using this tool, guided by data experimentation.

2. Launch Internal Data Products:

- Use Chronon extensively within Airbnb to ensure it effectively supports business operations.

3. Expose to Customers as Data-Driven Products/Features:

- Open-source Chronon to let other companies leverage the same efficiencies in their data and ML operations.

Bridging Digital (DX) and AI Transformations (AIX)

The progression from utilizing ML features to improve key performance indicators, to internal product deployment, and finally to launching customer-facing data-driven products, illustrates a powerful pathway for modern business growth. This development not only streamlines internal processes but also pioneers innovative product features that can redefine industry standards through the effective use of data experimentation.

Historically, we've seen the transformative impact of leveraging internal technological advances for external markets, as demonstrated by Amazon Web Services, which turned in-house cloud computing solutions into global infrastructure services. Similarly, the open-sourcing of Airbnb's Chronon platform shows how tools developed for internal efficiencies can catalyze broader industry advancements. Today, the mainstreaming of machine learning continues this trend, turning sophisticated internal data tools and experiments into public-facing products that not only improve user experiences but also create new business paradigms. This cycle of innovation underscores a universal theme: transformative technologies often begin as internal solutions before reshaping entire industries.

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About Me:

I am the founder and CEO of Paragon Technology Solutions - a CTO-led AI/ML advisory company helping mid market businesses play “moneyball” with their data. If we can help your business make smarter use of its data, please reach out [email protected].